Method for predicting occurrence of tool processing event and virtual metrology application and computer program product thereof

ABSTRACT

Embodiments of the present disclosure provide a method for predicting an occurrence of a tool processing event, thereby determining whether to activate a virtual metrology. In a model-building stage, plural sets of model-building data are used to create at least one classification model in accordance with at least one classification algorithm, in which each classification model includes plural decision trees. Then, probabilities of the decision trees are used to create at least one reliance index model, and the sets of model-building data are used to create at least one similarity index model in accordance with a statistical distance algorithm. In a conjecture stage, a set of processing data of a workpiece is inputted into each classification model, each reliance index model and each similarity index model to determine whether to activate (start) virtual metrology.

RELATED APPLICATIONS

The present application is based on, and claims priority from TaiwanApplication Serial Number 109118836, filed Jun. 4, 2020, the disclosureof which is hereby incorporated by reference herein in its entirety.

BACKGROUND Field of Invention

The present disclosure relates to a method for predicting an occurrenceof a tool processing event, and a virtual metrology application and acomputer program product thereof. More particularly, the presentdisclosure relates to a method for predicting an occurrence of a toolprocessing event, and a method for determining whether to activate avirtual metrology application and a computer program product thereofbased on an automated classification scheme.

Description of Related Art

Virtual metrology has been quite widely applied in various industries,such as a semiconductor industry and a tooling industry. Virtualmetrology can convert sampling inspections with metrology delay intoreal-time and on-line total inspections. For example, when virtualmetrology is introduced into a wafer-sawing process in the semiconductorindustry, process abnormalities can be found in real time and can beimproved in time, thereby preventing an entire wafer lot from beingscrapped subsequently. When virtual metrology is introduced into amachine tool, the quality precision of each processed workpiece (such asa vehicle wheel) can be conjectured for meeting the requirements realtime and accuracy, thereby predicting the processing quality of themachine tool to overcome the shortcomings of the conventional in-linemetrology and off-line metrology.

Although the conventional virtual metrology may be mostly suitable forits expected purposes, yet it still does not meet the requirements invarious aspects.

SUMMARY

An object of the present invention is to provide a method for correctlypredicting an occurrence of a tool processing event in real time,thereby determining whether to perform subsequent operations andtreatments in time.

Another object of the present invention is to provide a method and acomputer program product thereof for correctly determining whether toactivate a virtual metrology application in real time, thus avoidinguser misjudgments.

According to an aspect of the present invention, a method for predictingan occurrence of a tool processing event is provided. In the method, atfirst, plural sets of historical process data are obtained, in which thesets of historical process data are used or generated by a productiontool when plural historical workpieces are processed by the productiontool, and the sets of historical process data are one-to-onecorresponding to the sets of historical workpieces. Then, pluralhistorical processing event index values are obtained for indicating ifa processing event occurred when the production tool processed each ofthe historical workpieces, in which the historical processing eventindex values are one-to-one corresponding to the sets of historicalprocess data, and the historical processing event index values and thesets of historical process data respectively form plural sets ofmodel-building data. Thereafter, a model-building operation isperformed. In the model-building operation, a classification model isbuilt by using the sets of model-building data in accordance with aclassification algorithm, in which the classification model includesplural decision trees; and a reliance index model is built by usingprobabilities of the decision trees. Then, a conjecturing operation isperformed. In the conjecturing operation, at least one set of processdata is obtained, in which the at least one set of process data is usedor generated by the production tool when at least one workpiece isprocessed; the at least one set of process data is inputted into theclassification model, thereby obtaining at least one event predictedvalue used for indicating if the processing event occurs when theproduction tool is processing each of the at least one workpiece; andthe reliance index model is used to compute a reliance index value ofeach of the at least one event predicted value for indicating a reliancelevel of each of the at least one event predicted value.

In some embodiments, each of the sets of historical process dataincludes plural parameters, and each of the at least one set of processdata includes the parameters. In the aforementioned method forpredicting the occurrence of the tool processing event, a datapreprocessing operation is performed to convert values of the parametersin each of the sets of historical process data to first values of pluralparameter indicators by using plural algorithms, in which the parameterindicators are one-to-one corresponding to the algorithms, the sets ofmodel-building data including the historical processing event indexvalues and the first values of the parameter indicators converted fromeach of the sets of historical process data; and the data preprocessingoperation is performed to convert values of the parameters in each ofthe at least one set of process data to second values of the parameterindicators by using the algorithms, in which the conjecturing operationincludes inputting the second values of the parameter indicators intothe classification model, thereby obtaining the at least one eventpredicted value.

In some embodiments, the aforementioned model-building operation furtherincludes building a similarity model by using the sets of model-buildingdata in accordance with a statistical distance algorithm. Theaforementioned conjecturing operation further includes using thesimilarity model to compute a global similarity index between the setsof model-building data and the second values of the parameter indicatorsin each of the at least one set of process data, thereby indicatingdegrees of similarity between the sets of model-building data and thesecond values of the parameter indicators.

In some embodiments, the number of the at least one set of process datais greater than one, and the number of the at least one workpiece isgreater than one. The aforementioned method for predicting theoccurrence of the tool processing event further includes obtainingplural actual processing event index values used for indicating if theprocessing event occurred when the production tool processed each of theworkpieces; obtaining a correct rate of the event predicted valuesaccording to the actual processing event index values; checking if thesets of model-building data are imbalanced when the correct rate issmaller than a correct-rate threshold; adding the actual processingevent index values and the values of the parameters in theircorresponding sets of process data to the sets of model-building datawhen the sets of model-building data are imbalanced, and then performingthe model-building operation again; and adjusting the classificationmodel, the reliance index model and the similarity model by using theactual processing event index values and the values of the parameters intheir corresponding sets of process data to the sets of model-buildingdata, when the sets of model-building data are balanced.

In some embodiments, the aforementioned method for predicting theoccurrence of the tool processing event further includes performing anoversampling operation on the sets of model-building data to generate aplurality of sets of sample data similar to data in a minority class inthe sets of model-building data, thereby overcoming data imbalance ofthe sets of model-building data; and adding the sets of sample data tothe sets of model-building data.

According to another aspect of the present invention, a method fordetermining whether to activate a virtual metrology is provided. In themethod, plural sets of historical process data are obtained, in whichthe sets of historical process data are used or generated by aproduction tool when plural historical workpieces are processed by theproduction tool, and the sets of historical process data are one-to-onecorresponding to the sets of historical workpieces. Then, pluralhistorical processing event index values are obtained for indicating ifa processing event occurred when the production tool processed each ofthe historical workpieces, in which the historical processing eventindex values are one-to-one corresponding to the sets of historicalprocess data, and the historical processing event index values and thesets of historical process data respectively form a plurality of sets ofmodel-building data Thereafter, a model-building operation is performed.In the model-building operation, two classification models are built byusing the sets of model-building data in accordance with twoclassification algorithms, in which each of the classification modelsincludes plural decision trees; and two reliance index models are builtby using probabilities of the decision trees of each of theclassification models. Thereafter, a conjecturing operation isperformed. In the conjecturing operation, at least one set of processdata is obtained, in which the at least one set of process data is usedor generated by the production tool when at least one workpiece isprocessed; the at least one set of process data is inputted into theclassification models, thereby obtaining at least one set of eventpredicted values used for indicating if the processing event occurredwhen the production tool processed each of the at least one workpiece,each of the at least one set of event predicted values includes a firstevent predicted value and a second event predicted value; the relianceindex models are used to compute two reliance index values of each ofthe at least one set of event predicted values; one of the relianceindex values is selected as a composite reliance index value, the one ofthe reliance index values corresponding to one of the first eventpredicted value and the second event predicted value that has a smallerreliance level than the other one of the first event predicted value andthe second event predicted value; a step is performed to check if bothof the first event predicted value and the second event predicted valueindicate that the processing event will occur, thereby obtaining a firstchecking result; a virtual metrology is activated to conjecture qualityof the workpiece when the first checking result is true; a step isperformed to check if the composite reliance index value indicates thatthe one of the first event predicted value and the second eventpredicted value is smaller than a reliance index threshold when thefirst checking result is false, thereby obtaining a second checkingresult; and the virtual metrology is activated to conjecture quality ofthe workpiece when the second checking result is true.

In some embodiments, the aforementioned model-building operationincludes building two similarity models by using the sets ofmodel-building data in accordance with a statistical distance algorithm.The aforementioned method for determining whether to activate thevirtual metrology further includes respectively using the two similaritymodels to compute two global similarity indexes between the sets ofmodel-building data and the second values of the parameter indicators ineach of the at least one set of process data; selecting one of theglobal similarity indexes as a composite global similarity index value,the one of the global similarity indexes representing less degrees ofsimilarity between the sets of model-building data and the second valuesof the parameter indicators in each of the at least one set of processdata; checking if the composite global similarity index indicates thatthe degrees of similarity between the sets of model-building data andthe second values of the parameter indicators in each of the at leastone set of process data is smaller than a global similarity indexthreshold when the second checking result is false, thereby obtaining athird checking result; and activating the virtual metrology toconjecture quality of the workpiece when the third checking result istrue.

According to another aspect of the present invention, a computer programproduct stored on a non-transitory tangible computer readable recordingmedium is provided. When this computer program product is loaded andexecuted by a computer, the aforementioned method for determiningwhether to activate the virtual metrology is performed.

Hence, the application of the embodiments of the present disclosure cancorrectly predict an occurrence of a tool processing event in real time,thereby determining whether to perform subsequent operations andtreatments in time. The application of the embodiments of the presentdisclosure also can correctly determine whether to activate a virtualmetrology in real time, thus avoiding user misjudgments.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention can be more fully understood by reading the followingdetailed description of the embodiment, with reference made to theaccompanying drawings as follows:

FIG. 1 is a schematic block diagram for explaining a virtual metrologyapplication according to some embodiments of the disclosure;

FIG. 2A is schematic block diagram for explaining an automatedclassification scheme according to some embodiments of the disclosure;

FIG. 2B is a flow chart showing a model-building step related to theautomated classification scheme according to some embodiments of thedisclosure;

FIG. 3A is a schematic diagram for explaining a reliance index modelbased on a random forest (RF) algorithm according to some embodiments ofthe disclosure;

FIG. 3B is a schematic block diagram for explaining a reliance indexmodel based on an extreme gradient boosting (XGboost; XG) algorithmaccording to some embodiments of the disclosure;

FIG. 4 is schematic diagram for explaining a similarity model of processparameters according to some embodiments of the disclosure;

FIG. 5A and FIG. 5B are a flow chart showing a dual-phase method forpredicting an occurrence of a tool processing event according to someembodiments of the disclosure;

FIG. 6 is a flow chart showing a method for determining whether toactivate a virtual metrology application according to some embodimentsof the disclosure; and

FIG. 7 shows prediction results of an application example of thedisclosure.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

Reference will now be made in detail to the embodiments of the presentinvention, examples of which are illustrated in the accompanyingdrawings. Wherever possible, the same reference numbers are used in thedrawings and the description to refer to the same or like parts.

As used herein, the terms “first” and “second” do not intend to indicatea specific order or sequence, and are merely used for distinguishing thedevices or operations described by similar phraseology or terminologyherein.

Virtual metrology applied on a wafer sawing process is used forpredicting a wafer-chipping amount. During the wafer sawing process, notevery wafer (workpiece) would have a chipping event. However, becausenot recognizing whether the wafers have the occurrence of thewafer-chipping event, the virtual metrology generates a predictedwafer-chipping amount for each of the wafers, thus causing usermisjudgments. In a tooling processing, not every workpiece is processedby a machine tool to generate a processing action event of the machinetool. However, because not recognizing whether the machine tool has theoccurrence of the processing action event (i.e. the machine tool hascontacted and processed the workpiece), the virtual metrology stillgenerates a predicted accuracy value for each of the workpieces, thuscausing a user to make a misjudgment on the workpieces that are notprocessed by the machine tool. Therefore, a human judgment has to beadded to the virtual metrology for determining whether there is anoccurrence of a processing event (such as wafer chipping or a toolprocessing action, etc.), thereby avoid user misjudgments.

In the virtual metrology, plural sets of model-building samples are usedto build a virtual metrology model according to a conjecture algorithm.Each set of model-building samples includes a set of historical processdata and a historical actual measurement value. The sets of historicalprocess data are generated or used by a production tool when theproduction tool is processing historical workpieces. The sets ofhistorical process data are one-to-one corresponding to the historicalworkpieces. The historical actual measurement value is obtained afterone of the quality items of each historical workpiece is measured by ametrology tool. The conjecture (prediction) algorithm includes a neuralnetwork (NN) algorithm, a multi-regression (MR) algorithm, a partialleast square (PLS) algorithm or a support vector machines (SVM)algorithm. For the wafer sawing process, the production tool is a wafercutting tool; the historical actual measurement value (quality item) isa wafer-chipping amount; and the process data include blade clogging, acoolant flow rate, a spindle speed (RPM), a feeding rate, waferconditions (such as thickness, coating, etc.), and/or a kerf width. Forthe tool processing, the production tool is a machine tool; thehistorical actual measurement value(s) (quality item(s)) include(s)roughness, straightness, angularity, perpendicularity, parallelismand/or roundness; and the process data include a working current, and/orvibration data and/or audio frequency data obtained by three-axisaccelerometer sensors or acoustic sensors mounted on the machine tool.

Using the wafer sawing process as an example, when the sets ofmodel-building samples include the samples with no occurrence ofwafer-chipping event, the virtual metrology model (for predicting thewafer-chipping amounts) built by using the sets of model-buildingsamples based on the neural network (NN) algorithm has a mean absoluteerror (MAE) of 1.234. When the sets of model-building samples do notinclude the samples with no occurrence of wafer-chipping event, thevirtual metrology model (for predicting the wafer-chipping amounts)built by using the sets of model-building samples based on the neuralnetwork (NN) algorithm has a mean absolute error (MAE) of 1.067, whichis better than the case of the sets of model-building samples includingthe samples with no occurrence of wafer-chipping event. It can be knownfrom the above that the samples with no occurrence of wafer-chippingevent have significant impacts on the accuracy of the virtual metrology.

Thus, embodiments of the disclosure provide an automated classificationscheme for predicting there is an occurrence of a tool processing event,so as to determine whether to activate a virtual metrology or subsequentactions (such as shutdown, tool maintenance, etc.), in the toolprocessing event includes a wafer chipping event, or a tool processingaction event, etc.

The virtual metrology, global similarity index, DQI_(X) (process dataquality index) and DQI_(y) (metrology data quality index) used inembodiments of the disclosure hereinafter may refer to U.S. Pat. No.8,095,484 B2. Embodiments of the disclosure may be combined with thevirtual metrology with this US patent, i.e. U.S. Pat. No. 8,095,484 B2is hereby incorporated by reference.

Referring to FIG. 1, FIG. 1 is a schematic block diagram for explaininga virtual metrology application according to some embodiments of thedisclosure. At first, a set of process data 102 is obtained from aproduction tool 100, in which the set of process data 102 is generatedor used by the production tool 100 when the production tool 100 isprocessing a workpiece. The set of process data 102 includes sensordata. Then, an automated classification scheme (ACS) 110 uses the set ofprocess data 102 to predict whether the production tool 100 (such as amachine tool or a cutting tool) has an occurrence of a tool processingevent (such as a wafer chipping event or a tool processing actionevent). If there is the occurrence of the tool processing event, avirtual metrology 120 can be activated, such as an automatic virtualmetrology (AVM). Embodiments of the disclosure can automatically ifthere is an occurrence of a tool processing event, and the virtualmetrology 120 is activated only when there is the occurrence of the toolprocessing event, thereby avoid user misjudgments.

Referring to FIG. 2A, FIG. 2A is schematic block diagram for explainingan automated classification scheme according to some embodiments of thedisclosure. The automated classification scheme (ACS) 110 includes aprocess data preprocessing operation 202A, a processing event datapreprocessing operation 202B, classification models 210, a compositereliance index (RI_(C)) model 220 and a composite global similarityindex (GSI_(C)) model 230. The process data preprocessing operation 202Aperforms data quality evaluation on the set of process data 102 based onthe DQI_(X) model, and arranges and standardizes (z-score) the originalprocess data 102 from the production tool 100, deletes abnormal data andselects important parameters with the deletion of unimportantparameters, thereby preventing data interference that affects predictionaccuracy. The processing event data preprocessing operation 202Bperforms data quality evaluation on metrology data 104 based on theDQI_(y) model, and performs discretization and selection on themetrology data to delete the abnormal values therein. The metrology data104 may be, for example, actual measurement values of workpieces (suchas wafer chipping amounts, etc.) or the status of the production tool100 (such as working current, etc.). The processing event datapreprocessing operation 202B then converts the metrology data 104 toprocessing event index values used for indicating if processing eventsoccur when the production tool is processing the workpieces. Forexample, a processing event index value indicating a processing actionevent of the production tool is obtained based on the working currenti.e. when the working current is greater than or equal to a thresholdvalue, it means that the production tool has processed a workpiece, andthe processing event index value is such as “1”. A processing eventindex value indicating a wafer-chipping event is obtained based on thewafer-chipping amount, i.e. when the wafer-chipping amount is greaterthan or equal to a threshold value, it means that the wafer-chippingoccurs when the production tool is processing a wafer, and theprocessing event index value is such as “1”.

The automated classification scheme (ACS) 110 employs a dual-phaseoperation scheme and classification algorithms to conjecture aprocessing event index of a workpiece. The classification schemes can beselected from various classification algorithms such as a random forest(RF) algorithm and an extreme gradient boosting (XGboost; XG) algorithm.In some embodiments, the classification models 210 include a RF model210A and an XG model 210B for generating event predicted values CP_(XG)and CP_(RF) respectively. The RI_(C) Model is used to gauge the reliancelevels of the event predicted values CP_(XG) and CP_(RF) to generate acomposite reliance index (RI_(C)). The GS_(C) model 230 is used tocalculate the similarity between a newly-inputted set of process data102 and the sets of process data (model-building data) for modelbuilding or training in the classification models 210, and generates acomposite global similarity index (GSI_(C)) to judge whether thenewly-inputted process (parameters) data shift. The GSI_(C) is used toassist the RI_(C) to determine the degrees of confidence of theclassification models 210.

Hereinafter, the classification models 210, the RI_(C) model 220 and theGSI_(C) model 230 are explained.

Referring to FIG. 2B, FIG. 2B is a flow chart showing a model-buildingstep related to the automated classification scheme according to someembodiments of the disclosure. At first, a data collection operation 200is performed to obtain plural sets of historical process data and pluralhistorical processing event index values. The sets of historical processdata are used or generated by a production tool when plural historicalworkpieces are processed by the production tool, and the sets ofhistorical process data are one-to-one corresponding to the sets ofhistorical workpieces. The historical processing event index values areused to indicate if a processing event (such as a wafer-chipping event,a tool processing action event, etc.) occurred when the production toolprocessed each of the historical workpieces. Each set of historicalprocess data includes plural parameters, and the parameters are thehistorical process data used for building the virtual metrology modeldescribed above. The historical processing event index values areone-to-one corresponding to the sets of historical process data. Thehistorical processing event index values and their corresponding sets ofhistorical process data respectively form plural sets of model-buildingdata.

Thereafter, a data preprocessing operation 202 is performed to convertvalues of the parameters in each of the sets of historical process datato values of plural parameter indicators by using plural algorithms, inwhich the parameter indicators are one-to-one corresponding to thealgorithms. The parameter indicators include a k-times frequency (wherek is greater than 0) after conversion to a frequency domain, a kurtosisof statistic distribution, a skewness of statistic distribution, astandard deviation, a root mean square (RMS), a mean value, a maximumvalue, and/or a minimum value. After the data preprocessing operation202 is performed, the sets of model-building data include the historicalprocessing event index values and the values of their correspondingparameter indicators.

Then, an oversampling operation 204 is performed on the sets ofmodel-building data to generate plural sets of sample data similar todata in a minority class in the sets of model-building data, therebyovercoming data imbalance of the sets of model-building data. Theoversampling operation 204 may adopt a borderline-SMOTE (SyntheticMinority Oversampling Technique). The borderline-SMOTE is well known tothose who are skilled in the art, and thus is described in detailherein.

Thereafter, the sets of sample data are added to the sets ofmodel-building data for performing model-building operations 240A and240B. The model-building operation 240A builds a classification model(such as a RF model) in accordance with a classification algorithm (suchas the RF algorithm) based on bagging (from bootstrap aggregating) byusing the sets of model-building data. The model-building operation 240Bbuilds a classification model (such as a XG model) in accordance with aclassification algorithm (such as the XG algorithm) based on boosting byusing the sets of model-building data. The classification models (the RFmodel and the XG model) include plural RF decision trees 310A and pluralXG decision trees 310B.

Referring to FIG. 3A and FIG. 3B, FIG. 3A and FIG. 3B are schematicdiagrams for explaining reliance index model based on the RF algorithmand the XG algorithm according to some embodiments of the disclosure.The model-building operations 240A and 240B build two reliance indexmodels (a RI_(RF) model and a RI_(X G) model) by using probabilities ofthe decision trees of the RF model and the XG model respectively,thereby computing the composite reliance index (RI_(C)). Hereinafter,the wafer sawing process is used as an example for explaining thecomputation method of the composite reliance index (RI_(C)), “Chipping”represents there is an occurrence of a tool processing event, and abinary value of its processing event index value is such as “1”;“Nonchipping” represents there is no occurrence of a tool processingevent, and a binary value of its processing event index value is such as“0”. The composite reliance index value (RI_(C)) is computed by thefollowing equations.

$\begin{matrix}{{RI}_{RF} = {\max\left( {\frac{\Sigma\;{PRF}_{{Chipping}_{i}}}{n},\frac{\Sigma\;{PRF}_{{Nonchipping}_{i}}}{n}} \right)}} & (1) \\{{RI}_{XG} = {\max\left( {{\sum{PXG}_{{Chipping}_{i}}}\ ,\ {\sum\;{PXG}_{{Nonchipping}_{i}}}} \right)}} & (2) \\{{RI}_{C} = {\min\left( {{RI}_{RF},\ {RI}_{XG}} \right)}} & (3)\end{matrix}$

where n stands for the amount of the RF decision trees 310A;PRF_(chippingi) stands for a chipping probability of the i^(th) RFdecision tree 310A; PRF_(Nonchippingi) stands for a non-chippingprobability of the i^(th) RF decision tree 310A; PXG_(chippingi) standsfor a chipping probability of the i^(th) XG decision tree 310B; andPXG_(Nonchippingi) stands for a non-chipping probability of the i^(th)XG decision tree 310B.

RI_(C) is the minimal classification probability of RI_(XG) and RI_(RF),in which RI_(RF) is a reliance index value of the RF model, and RI_(XG)is a reliance index value of the XG model. In theory, it is classifiedto a certain category when the classification probability is greaterthan 0.5. However, the data slightly over 0.5 might be located at theclassification boundary and that might cause misclassification. Toensure the correctness of classification, the threshold is set as 0.7.It is worthy to be noted that embodiments of the disclosure also can useone of the reliance index models (RI_(XG) and RI_(RF)) alone to indicatethe reliance level of the classification predicted value.

The model-building operations 240A and 240B build two similarity models(such as GSI_(XG) and GSI_(RF) models) by using the sets ofmodel-building data of the XG model and the RF model in accordance witha statistical distance algorithm, thereby computing a composite globalsimilarity index value (GSI_(C)) for determining the similarity betweenthe newly-inputted set of process data and the historical process datain the respective sets of model-building data. Referring to FIG. 4, FIG.4 is schematic diagram for explaining a similarity model of processparameters according to some embodiments of the disclosure.

For example, a K means clustering algorithm is adopted to calculate ahistorical data center point 404 of plural historical data points 400(for each set of model building data of the RF model or the XG model).After a new data point 410 is added, for example, the K means clusteringalgorithm is used again to calculate a new data center point 414. Then,the statistical distance algorithm is used to build similarity models(GSI_(XG) and GSI_(RF) models), thereby obtaining the distance betweenthe historical data center point 404 and the new data center point 414.The statistical distance algorithm may be such as a Mahalanobis distancealgorithm or a standardized Euclidean distance algorithm. TheMahalanobis distance algorithm is a useful way of determining similarityof an unknown sample set to a known sample set. This method considersthe correlation of the data sets and is scale-invariant, namely it isnot dependent on the scale of measurements. If the data set has highsimilarity, the calculated Mahalanobis distance calculated will berelatively small. The composite global similarity index value (GSI_(C))is computed by the following equations.

SI_(RF)=1d(OriCenter _(RF), NewCenter _(RF))   (4)

SI_(XG)=1d(OriCenter _(XG), NewCenter _(XG))   (5)

GSI_(C)=min(SI_(RF), SI_(XG))   (6)

where d(X1, X2) is a statistical distance between two data points X1 andX2; OriCenter _(RF) is a center point of the model-building data for theRF model; NewCenter _(RF) is a center point of the model-building datafor the RF model and the new data point; OriCenter _(XG) is a centerpoint of the model-building data for the XG model; NewCenter _(XG) is acenter point of the model-building data for the XG model and the newdata point; SI_(RF) is the global similarity index value between the newdata point and the model-building data for the RF model; SI_(XG) is theglobal similarity index value between the new data point and themodel-building data for the XG model.

Embodiments of the disclosure uses a cross validation's leave-one-out(LOO) method to compute a global similarity index threshold (GSI_(T)).The global similarity index threshold (GSI_(T)) used in embodiments ofthe disclosure hereinafter may refer to U.S. Pat. No. 8,095,484 B2.Embodiments of the disclosure may be combined with the virtual metrologywith this US patent, i.e. U.S. Pat. No. 8,095,484 B2 is herebyincorporated by reference. For the sake of convenient GSI_(C)management, the composite global similarity index value (GSI_(C)) isnormalized to vary from 1 (maximum) to 0 (minimum), in which the higherGSI _(C) indicates more similarity between the new data point and themodel-building data for the classification mode. In some embodiments,the GSI_(T) is mapped to 0.7, i.e. when the GSI_(C) is smaller than 0.7,the similarity between the new data point and the model-building datafor the classification model is low, and there are larger deviations inthe parameters of the new data from those of the model-building data,thus affecting the prediction correctness of the classification model.It is noted that those who are skilled in the art also may varyequations (4) and (5) to show that the higher value of the GSI_(C)indicates less similarity between the new data point and themodel-building data for the classification model.

After the model-building operations have built the classificationmodels, the RI_(C) model and the GSI_(C) model, a conjecturing operationis performed to predict if a tool processing event occurs. FIG. 5A andFIG. 5B are a flow chart showing a dual-phase method for predicting anoccurrence of a tool processing event according to some embodiments ofthe disclosure. In a first phase, a process data collection operation510 is performed to obtain a set of process data, in which the set ofprocess data is used or generated by the production tool when aworkpiece is processed by the production tool. The set of process dataincludes the same parameters of the sets of historical process data.Then, operation 512 is performed to determine if the process datacollection of the workpiece is completed. When the result of operation512 is yes, operation 514 is performed to input the set of process datainto the classification models (such as the RF model and the XG model),the RI_(C) model and the GSI_(C) model, thereby obtaining one or twoevent predicted values (CP_(XG) and/or CP_(RF)) and their accompanyingcomposite global similarity index value (GSI_(C)) and composite relianceindex value (RI_(C)) for indicating whether there is an occurrence of aprocessing event when the production tool is processing the workpiece.Before the set of process data is inputted into the classificationmodels, the data preprocessing operation 202 as shown in FIG. 2B can beperformed to convert values of the parameters in the set of process datato values of the aforementioned parameter indicators by using theaforementioned algorithms. When the result of operation 512 is no orafter operation 514 is completed, the dual-phase method returns to theprocess data collection operation 510 to obtain another set of processdata used or generated by the production tool when a next workpiece isprocessed by the production tool.

In a second phase, at first, a metrology data collection operation 520is performed to obtain actual metrology data of the workpiece processedin the first phase or the production tool processing the workpiece. Theactual metrology data may be an actual measurement value of theworkpiece (such as a wafer chipping amount, etc.) or the status of theproduction tool 100 (such as working current, etc.). Then, operation 522is performed to determine if the collection of the actual metrology datais completed. When the result of operation 522 is yes, operation 524 isperformed to convert the actual metrology data to a processing eventindex value used for indicating if a processing event occurs when theproduction tool is processing the workpiece. For example, when thewafer-chipping amount is greater than or equal to a threshold value, itmeans that the wafer-chipping occurs when the production tool isprocessing the workpiece, and the processing event index value is suchas “1”; and, when the wafer-chipping amount is smaller than thethreshold value, it means that no wafer-chipping occurs when theproduction tool is processing the workpiece, and the processing eventindex value is such as “0”. For another example, when the workingcurrent is greater than or equal to a threshold value, it means that theproduction tool has processed a workpiece (has a processing action), andthe processing event index value is such as “1”; and when the workingcurrent is smaller than the threshold value, it means that theproduction tool does not process the workpiece (has no processingaction), and the processing event index value is such as “0”.

Thereafter, operation 526 is performed to determine if actual metrologydata of k pieces of workpieces and their corresponding k event predictedvalues have been obtained, in which k is greater than and may be such as10. When the result of operation 526 is yes, operation 528 is performedto calculate a correct rate of these event predicted values, and thecorrect rate is calculated by the following equations.

$\begin{matrix}{{Sensitiviy} = {\frac{TP}{{TP} + {FN}} \times 100}} & (7) \\{{Specificity} = {\frac{TN}{{TN} + {FP}} \times 100}} & (8) \\{{{correct}\mspace{14mu}{rate}} = \frac{{Sensitivity} + {Specificity}}{2}} & (9)\end{matrix}$

where TP stands for the number of samples of true positive which is anoutcome where there is an occurrence of the tool processing event, andthe event predicted value predicts the same; TN stands for the number ofsamples of true negative which is an outcome where there is nooccurrence of the tool processing event, and the event predicted valuepredicts the same; FP stands for the number of samples of false positivewhich is an outcome where there is no occurrence of the tool processingevent, but the event predicted value predicts the opposite; FN standsfor the number of samples of false negative which is an outcome wherethere is an occurrence of the tool processing event, but the eventpredicted value predicts the opposite. Thereafter, operation 530 isperformed to check if the correct rate is smaller than a correct-ratethreshold (CR_(T)). When the result of operation 530, 526 or 522 is no,the dual-phase method returns to the metrology data collection operation520 to obtain actual metrology data of a next workpiece or theproduction tool processing the next workpiece.

When the result of operation 530 is yes, operation 540 is performed tocheck if the sets of model-building data are imbalanced. When the resultof operation 540 is yes, the actual processing event index values andthe values of the parameters in their corresponding sets of process dataare added to the sets of model-building data, and then themodel-building operation is performed again (operation 542) to retrainone of the classification models, one of the reliance index models andone of the global similarity models corresponding to the correct rate.When the result of operation 540 is no, one of the classificationmodels, one of the reliance index models and one of the similaritymodels are adjusted by using the actual processing event index valuesand the values of the parameters in their corresponding sets of processdata to the sets of model-building data (operation 544). In someembodiments, a data-imbalance ratio is to 1:3, i.e. when the occurrencerate of wafer chipping event in the sets of model-building data is lessthan 25%, the sets of model-building data are imbalanced, and the modelsneed retraining. After operations 542 and 544 are performed, theclassification model(s), the reliance index model(s) and the similarityindex model(s) in the first phase can be updated (operation 550).

It is worthy to be noted that, in the first phase, two classificationmodels from two different classification algorithms and their relianceindex models and similarity models may be used. Thus, the second phasemay be performed with respect to the two classification models and theirreliance index models and similarity models.

Because the samples without the occurrence of processing event havesignificant impacts on the accuracy of virtual metrology, embodiments ofthe disclosure determines whether to activate a virtual metrology byusing the aforementioned method for predicting an occurrence of a toolprocessing event. Referring to FIG. 6, FIG. 6 is a flow chart showing amethod for determining whether to activate a virtual metrologyapplication according to some embodiments of the disclosure. At first,operation 610 is performed to obtain a set of event predicted values ofa workpiece and its accompanying composite global similarity index value(GSI_(C)) and composite reliance index value (RI_(C)), in which the setof event predicted values includes a first event predicted value (forexample, CP_(RF)) and a second event predicted value (for example,CP_(XG)). Thereafter, operation 620 is performed to check if both of thefirst event predicted value and the second event predicted valueindicate that the processing event will occur. When the result ofoperation 620 is yes, a virtual metrology is activated to conjecture thequality of the workpieces. When the result of operation 620 is no,operation 620 is performed to check if the composite reliance indexvalue (RI_(C)) is smaller than a reliance index threshold (for example,0.7). When the result of operation 630 is yes, the virtual metrology isactivated. When the result of operation 630 is no, operation 640 isperformed to check if the composite global similarity index value(GSI_(C)) is smaller than a global similarity index threshold (forexample, 0.7). When the result of operation 640 is yes, the virtualmetrology is activated. When the result of operation 640 is no, thevirtual metrology is not activated.

Hereinafter, an application example of wafer chipping is used to explainthe embodiments of the disclosure. Referring to FIG. 7, FIG. 7 showsprediction results of the application example of the disclosure, inwhich “*” represents an actual processing event index value; “x”represents an actual wafer-chipping amount; a curve 700 shows thecomposite reliance index values (RI_(C)) of respective workpieces; astraight line 702 shows a reliance index threshold; a curve 706 showsthe composite global similarity index values (GSI_(C)) of the respectiveworkpieces; a straight line 708 shows a global similarity indexthreshold; a curve 710 shows first event predicted values (CP_(RF)) ofthe respective workpieces, and a curve 720 shows second event predictedvalues (CP_(XG)) of the respective workpieces, in which the curves 710and 720 are overlapped as one curve except at a workpiece number P23; acurve 730 shows virtual metrology values (predicted wafer-chippingamounts) of the respective workpieces by using a neural network (NN)algorithm; and a curve 740 shows virtual metrology values (predictedwafer-chipping amounts) of the respective workpieces by using a partialleast square (PLS) algorithm. Except at a workpiece number P14 in anarea A3 and at a workpiece number P23 in an area A5, the first eventpredicted values (CP_(RF)) and the second event predicted values(CP_(XG)) match with the actual processing event index values, in whichthe correct rate of the first event predicted values (CP_(RF)) is0.9714, and the correct rate of the second event predicted values(CP_(XG)) is 0.9428.

The event predicted values of the workpieces in areas A2, A4 and A6 allindicate no occurrence of wafer chipping events, and thus no virtualmetrology is performed, i.e. the wafer-chipping amounts are notpredicted. The composite reliance index values (RI_(C)) of workpiecenumbers P3 and P4 in an area A1 and workpiece numbers P21 and P22 in anarea A5 are poor (<0.7), but their CP_(RF) and CP_(XG) all indicate thatthere are occurrences of wafer chipping events, and thus the virtualmetrology is performed to predict the wafer-chipping amounts. TheCP_(RF) and CP_(XG) of the workpiece number P14 in the area A3 bothindicate no occurrence of wafer chipping event (which does not matchwith the actual processing event index), but its RI_(C) and GSI_(C) arepoor (<0.7), thus the virtual metrology is still performed to predictthe wafer-chipping amount. The CP_(RF) of a workpiece number P23 in thearea A5 matches with the actual processing event index, and its CP_(XG)does not match with the actual processing event index, but its RI_(C) ispoor (<0.7), and thus the virtual metrology is performed to predict thewafer-chipping amount. Apparently, the RI_(C) and GSI_(C) of theworkpiece number P14 and the RI_(C) of the workpiece number P23 canmodify the problem of inaccurate CP_(RF) and CP_(XG). Therefore, it canbe known from this application example that embodiments of thedisclosure can effectively predict the occurrence of the tool processingevents, and can accurately determine whether to activate a virtualmetrology.

It is understood that the method for predicting an occurrence of a toolprocessing event, and the method for determining whether to activate avirtual metrology application are performed by the aforementionedoperations. A computer program of the present disclosure stored on anon-transitory tangible computer readable recording medium is used toperform the method described above. The aforementioned embodiments canbe provided as a computer program product, which may include amachine-readable medium on which instructions are stored for programminga computer (or other electronic devices) to perform a process based onthe embodiments of the present disclosure. The machine-readable mediumcan be, but is not limited to, a floppy diskette, an optical disk, acompact disk-read-only memory (CD-ROM), a magneto-optical disk, aread-only memory (ROM), a random access memory (RAM), an erasableprogrammable read-only memory (EPROM), an electrically erasableprogrammable read-only memory (EEPROM), a magnetic or optical card, aflash memory, or another type of media/machine-readable medium suitablefor storing electronic instructions. Moreover, the embodiments of thepresent disclosure also can be downloaded as a computer program product,which may be transferred from a remote computer to a requesting computerby using data signals via a communication link (such as a networkconnection or the like).

It is also noted that the present disclosure also can be described inthe context of a manufacturing system. Although the present disclosuremay be implemented in semiconductor fabrication, the present disclosureis not limited to implementation in semiconductor fabrication and may beapplied to other manufacturing industries, in which the manufacturingsystem is configured to fabricate workpieces or products including, butnot limited to, microprocessors, memory devices, digital signalprocessors, application specific integrated circuits (ASICs), or othersimilar devices. The present disclosure may also be applied toworkpieces or manufactured products other than semiconductor devices,such as vehicle wheels, screws. The manufacturing system includes one ormore processing tools that may be used to form one or more products, orportions thereof, in or on the workpieces (such as wafers, glasssubstrates). Persons of ordinary skill in the art should appreciate thatthe processing tools may be implemented in any number of entities of anytype, including lithography tools, deposition tools, etching tools,polishing tools, annealing tools, machine tools, and the like. In theembodiments, the manufacturing system also includes one or moremetrology tools, such as scatterometers, ellipsometers, scanningelectron microscopes, and the like.

It can be known from the above that, the application of the embodimentsof the present invention can correctly predict an occurrence of a toolprocessing event in real time, thereby determining whether to performsubsequent operations and treatments in time. The application of theembodiments of the present disclosure also can correctly determinewhether to activate a virtual metrology in real time, thus avoiding usermisjudgments.

It will be apparent to those skilled in the art that variousmodifications and variations can be made to the structure of the presentinvention without departing from the scope or spirit of the invention.In view of the foregoing, it is intended that the present inventioncover modifications and variations of this invention provided they fallwithin the scope of the following claims and their equivalents.

What is claimed is:
 1. A method for predicting an occurrence of a toolprocessing event, the method comprising: obtaining a plurality of setsof historical process data, wherein the sets of historical process dataare used or generated by a production tool when a plurality ofhistorical workpieces are processed by the production tool, and the setsof historical process data are one-to-one corresponding to the sets ofhistorical workpieces; obtaining a plurality of historical processingevent index values used for indicating if a processing event occurredwhen the production tool processed each of the historical workpieces,wherein the historical processing event index values are one-to-onecorresponding to the sets of historical process data, and the historicalprocessing event index values and the sets of historical process datarespectively form a plurality of sets of model-building data; performinga model-building operation, comprising: building a classification modelby using the sets of model-building data in accordance with aclassification algorithm, wherein the classification model comprises aplurality of decision trees; and building a reliance index model byusing probabilities of the decision trees; and performing a conjecturingoperation, comprising: obtaining at least one set of process data,wherein the at least one set of process data is used or generated by theproduction tool when at least one workpiece is processed; inputting theat least one set of process data into the classification model, therebyobtaining at least one event predicted value used for indicating if theprocessing event occurs when the production tool is processing each ofthe at least one workpiece; and using the reliance index model tocompute a reliance index value of each of the at least one eventpredicted value for indicating a reliance level of each of the at leastone event predicted value.
 2. The method of claim 1, wherein each of thesets of historical process data comprises a plurality of parameters, andeach of the at least one set of process data comprises the parameters,the method further comprising: performing a data preprocessing operationto convert values of the parameters in each of the sets of historicalprocess data to first values of a plurality of parameter indicators byusing a plurality of algorithms, wherein the parameter indicators areone-to-one corresponding to the algorithms, the sets of model-buildingdata comprising the historical processing event index values and thefirst values of the parameter indicators converted from each of the setsof historical process data; and performing the data preprocessingoperation to convert values of the parameters in each of the at leastone set of process data to second values of the parameter indicators byusing the algorithms, wherein the conjecturing operation comprisesinputting the second values of the parameter indicators into theclassification model, thereby obtaining the at least one event predictedvalue.
 3. The method of claim 2, wherein the model-building operationcomprises: building a similarity model by using the sets ofmodel-building data in accordance with a statistical distance algorithm;the conjecturing operation further comprising: using the similaritymodel to compute a global similarity index between the sets ofmodel-building data and the second values of the parameter indicators ineach of the at least one set of process data, thereby indicating degreesof similarity between the sets of model-building data and the secondvalues of the parameter indicators.
 4. The method of claim 3, whereinthe number of the at least one set of process data is greater than one,and the number of the at least one workpiece is greater than one, themethod further comprising: obtaining a plurality of actual processingevent index values used for indicating if the processing event occurredwhen the production tool processed each of the workpieces; obtaining acorrect rate of the event predicted values according to the actualprocessing event index values; checking if the sets of model-buildingdata are imbalanced when the correct rate is smaller than a correct-ratethreshold; adding the actual processing event index values and thevalues of the parameters in their corresponding sets of process data tothe sets of model-building data when the sets of model-building data areimbalanced, and then performing the model-building operation again; andadjusting the classification model, the reliance index model and thesimilarity model by using the actual processing event index values andthe values of the parameters in their corresponding sets of process datato the sets of model-building data, when the sets of model-building dataare balanced.
 5. The method of claim 1, further comprising: performingan oversampling operation on the sets of model-building data to generatea plurality of sets of sample data similar to data in a minority classin the sets of model-building data, thereby overcoming data imbalance ofthe sets of model-building data; and adding the sets of sample data tothe sets of model-building data.
 6. A method for determining whether toactivate a virtual metrology, the method comprising: obtaining aplurality of sets of historical process data, wherein the sets ofhistorical process data are used or generated by a production tool whena plurality of historical workpieces are processed by the productiontool, and the sets of historical process data are one-to-onecorresponding to the sets of historical workpieces; obtaining aplurality of historical processing event index values used forindicating if a processing event occurred when the production toolprocessed each of the historical workpieces, wherein the historicalprocessing event index values are one-to-one corresponding to the setsof historical process data, and the historical processing event indexvalues and the sets of historical process data respectively form aplurality of sets of model-building data; performing a model-buildingoperation, comprising: building two classification models by using thesets of model-building data in accordance with two classificationalgorithms, wherein each of the classification models comprises aplurality of decision trees; and building two reliance index models byusing probabilities of the decision trees of each of the classificationmodels; and performing a conjecturing operation, comprising: obtainingat least one set of process data, wherein the at least one set ofprocess data is used or generated by the production tool when at leastone workpiece is processed; inputting the at least one set of processdata into the classification models, thereby obtaining at least one setof event predicted values used for indicating if the processing eventoccurred when the production tool processed each of the at least oneworkpiece, each of the at least one set of event predicted valuescomprising a first event predicted value and a second event predictedvalue; using the reliance index models to compute two reliance indexvalues of each of the at least one set of event predicted values;selecting one of the reliance index values as a composite reliance indexvalue, the one of the reliance index values corresponding to one of thefirst event predicted value and the second event predicted value thathas a smaller reliance level than the other one of the first eventpredicted value and the second event predicted value; checking if bothof the first event predicted value and the second event predicted valueindicate that the processing event will occur, thereby obtaining a firstchecking result; activating a virtual metrology to conjecture quality ofthe workpiece when the first checking result is true; checking if thecomposite reliance index value indicates that the one of the first eventpredicted value and the second event predicted value is smaller than areliance index threshold when the first checking result is false,thereby obtaining a second checking result; and activating the virtualmetrology to conjecture quality of the workpiece when the secondchecking result is true.
 7. The method of claim 6, wherein each of thesets of historical process data comprises a plurality of parameters, andeach of the at least one set of process data comprises the parameters,the method further comprising: performing a data preprocessing operationto convert values of the parameters in each of the sets of historicalprocess data to first values of a plurality of parameter indicators byusing a plurality of algorithms, wherein the parameter indicators areone-to-one corresponding to the algorithms, the sets of model-buildingdata comprising the historical processing event index values and thefirst values of the parameter indicators converted from each of the setsof historical process data; and performing the data preprocessingoperation to convert values of the parameters in each of the at leastone set of process data to second values of the parameter indicators byusing the algorithms, wherein the conjecturing operation comprisesinputting the second values of the parameter indicators into theclassification model, thereby obtaining the at least one event predictedvalue.
 8. The method of claim 7, wherein the model-building operationcomprises: building two similarity models by using the sets ofmodel-building data in accordance with a statistical distance algorithm;the method further comprising: respectively using the two similaritymodels to compute two global similarity indexes between the sets ofmodel-building data and the second values of the parameter indicators ineach of the at least one set of process data; selecting one of theglobal similarity indexes as a composite global similarity index value,the one of the global similarity indexes representing less degrees ofsimilarity between the sets of model-building data and the second valuesof the parameter indicators in each of the at least one set of processdata; checking if the global similarity index indicates that the degreesof similarity between the sets of model-building data and the secondvalues of the parameter indicators in each of the at least one set ofprocess data is smaller than a global similarity index threshold whenthe second checking result is false, thereby obtaining a third checkingresult; and activating the virtual metrology to conjecture quality ofthe workpiece when the third checking result is true.
 9. The method ofclaim 8, wherein the number of the at least one set of process data isgreater than one, and the number of the at least one workpiece isgreater than one, the method further comprising: obtaining a pluralityof actual processing event index values used for indicating if theprocessing event occurs when the production tool is processing each ofthe workpieces; obtaining a first correct rate of the first eventpredicted values and a second correct rate of the second event predictedvalues according to the actual processing event index values; checkingif the sets of model-building data are imbalanced when the first correctrate or the first correct rate is smaller than a correct-rate threshold;adding the actual processing event index values and the values of theparameters in their corresponding sets of process data to the sets ofmodel-building data when the sets of model-building data are imbalanced,and then performing the model-building operation again to retrain one ofthe classification models, one of the reliance index models and one ofthe global similarity models corresponding to the first correct rate orthe second correct rate; and adjusting the other one of theclassification models, the other one of the reliance index models andthe other one of the similarity models by using the actual processingevent index values and the values of the parameters in theircorresponding sets of process data to the sets of model-building data,when the sets of model-building data are balanced.
 10. The method ofclaim 6, further comprising: performing an oversampling operation on thesets of model-building data to generate a plurality of sets of sampledata similar to data in a minority class in the sets of model-buildingdata, thereby overcoming data imbalance of the sets of model-buildingdata; and adding the sets of sample data to the sets of model-buildingdata.
 11. The method of claim 6, wherein the classification algorithmsare a random forest algorithm and an extreme gradient boosting (XGboost)algorithm.
 12. A computer program product stored on a non-transitorytangible computer readable recording medium, which, when executed,performs a method for determining whether to activate a virtualmetrology, the method comprising: obtaining a plurality of sets ofhistorical process data, wherein the sets of historical process data areused or generated by a production tool when a plurality of historicalworkpieces are processed by the production tool, and the sets ofhistorical process data are one-to-one corresponding to the sets ofhistorical workpieces; obtaining a plurality of historical processingevent index values used for indicating if a processing event occurredwhen the production tool processed each of the historical workpieces,wherein the historical processing event index values are one-to-onecorresponding to the sets of historical process data, and the historicalprocessing event index values and the sets of historical process datarespectively form a plurality of sets of model-building data; performinga model-building operation, comprising: building two classificationmodels by using the sets of model-building data in accordance with twoclassification algorithms, wherein each of the classification modelscomprises a plurality of decision trees; and building two reliance indexmodels by using probabilities of the decision trees of each of theclassification models; and performing a conjecturing operation,comprising: obtaining at least one set of process data, wherein the atleast one set of process data is used or generated by the productiontool when at least one workpiece is processed; inputting the at leastone set of process data into the classification models, therebyobtaining at least one set of event predicted values used for indicatingif the processing event occurred when the production tool processed eachof the at least one workpiece, each of the at least one set of eventpredicted values comprising a first event predicted value and a secondevent predicted value; using the reliance index models to compute tworeliance index values of each of the at least one set of event predictedvalues; selecting one of the reliance index values as a compositereliance index value, the one of the reliance index values correspondingto one of the first event predicted value and the second event predictedvalue that has a smaller reliance level than the other one of the firstevent predicted value and the second event predicted value; checking ifboth of the first event predicted value and the second event predictedvalue indicate that the processing event will occur, thereby obtaining afirst checking result; activating a virtual metrology to conjecturequality of the workpiece when the first checking result is true;checking if the composite reliance index value indicates that the one ofthe first event predicted value and the second event predicted value issmaller than a reliance index threshold when the first checking resultis false, thereby obtaining a second checking result; and activating thevirtual metrology to conjecture quality of the workpiece when the secondchecking result is true.
 13. The computer program product of claim 12,wherein each of the sets of historical process data comprises aplurality of parameters, and each of the at least one set of processdata comprises the parameters, the method further comprising: performinga data preprocessing operation to convert values of the parameters ineach of the sets of historical process data to first values of aplurality of parameter indicators by using a plurality of algorithms,wherein the parameter indicators are one-to-one corresponding to thealgorithms, the sets of model-building data comprising the historicalprocessing event index values and the first values of the parameterindicators converted from each of the sets of historical process data;and performing the data preprocessing operation to convert values of theparameters in each of the at least one set of process data to secondvalues of the parameter indicators by using the algorithms, wherein theconjecturing operation comprises inputting the second values of theparameter indicators into the classification model, thereby obtainingthe at least one event predicted value.
 14. The computer program productof claim 13, wherein the model-building operation comprises: buildingtwo similarity models by using the sets of model-building data inaccordance with a statistical distance algorithm; the method furthercomprising: respectively using the two similarity models to compute twoglobal similarity indexes between the sets of model-building data andthe second values of the parameter indicators in each of the at leastone set of process data; selecting one of the global similarity indexesas a composite global similarity index value, the one of the globalsimilarity indexes representing less degrees of similarity between thesets of model-building data and the second values of the parameterindicators in each of the at least one set of process data; checking ifthe composite global similarity index indicates that the degrees ofsimilarity between the sets of model-building data and the second valuesof the parameter indicators in each of the at least one set of processdata is smaller than a global similarity index threshold when the secondchecking result is false, thereby obtaining a third checking result; andactivating the virtual metrology to conjecture quality of the workpiecewhen the third checking result is true.
 15. The computer program productof claim 14, wherein the number of the at least one set of process datais greater than one, and the number of the at least one workpiece isgreater than one, the method further comprising: obtaining a pluralityof actual processing event index values used for indicating if theprocessing event occurs when the production tool is processing each ofthe workpieces; obtaining a first correct rate of the first eventpredicted values and a second correct rate of the second event predictedvalues according to the actual processing event index values; checkingif the sets of model-building data are imbalanced when the first correctrate or the first correct rate is smaller than a correct-rate threshold;adding the actual processing event index values and the values of theparameters in their corresponding sets of process data to the sets ofmodel-building data when the sets of model-building data are imbalanced,and then performing the model-building operation again to retrain one ofthe classification models, one of the reliance index models and one ofthe global similarity models corresponding to the first correct rate orthe second correct rate; and adjusting the other one of theclassification models, the other one of the reliance index models andthe other one of the similarity models by using the actual processingevent index values and the values of the parameters in theircorresponding sets of process data to the sets of model-building data,when the sets of model-building data are balanced.
 16. The computerprogram product of claim 12, the method further comprising: performingan oversampling operation on the sets of model-building data to generatea plurality of sets of sample data similar to data in a minority classin the sets of model-building data, thereby overcoming data imbalance ofthe sets of model-building data; and adding the sets of sample data tothe sets of model-building data.
 17. The computer program product ofclaim 12, wherein the classification algorithms are a random forestalgorithm and an extreme gradient boosting (XGboost) algorithm.